cqplot: Chi Square Quantile-Quantile plots
In heplots: Visualizing Hypothesis Tests in Multivariate Linear Models

cqplot

R Documentation

Chi Square Quantile-Quantile plots

Description

A chi square quantile-quantile plots show the relationship between data-based values which should be distributed as \chi^2 and corresponding quantiles from the \chi^2 distribution. In multivariate analyses, this is often used both to assess multivariate normality and check for outliers, using the Mahalanobis squared distances (D^2) of observations from the centroid.

Usage

cqplot(x, ...)

## S3 method for class 'mlm'
cqplot(x, ...)

## Default S3 method:
cqplot(
  x,
  method = c("classical", "mcd", "mve"),
  detrend = FALSE,
  pch = 19,
  col = palette()[1],
  cex = par("cex"),
  ref.col = "red",
  ref.lwd = 2,
  conf = 0.95,
  env.col = "gray",
  env.lwd = 2,
  env.lty = 1,
  env.fill = TRUE,
  fill.alpha = 0.2,
  fill.color = trans.colors(ref.col, fill.alpha),
  labels = if (!is.null(rownames(x))) rownames(x) else 1:nrow(x),
  id.n,
  id.method = "y",
  id.cex = 1,
  id.col = palette()[1],
  xlab,
  ylab,
  main,
  what = deparse(substitute(x)),
  ylim,
  ...
)

Arguments

`x`	either a numeric data frame or matrix for the default method, or an object of class `"mlm"` representing a multivariate linear model. In the latter case, residuals from the model are plotted.
`...`	Other arguments passed to methods
`method`	estimation method used for center and covariance, one of: `"classical"` (product-moment), `"mcd"` (minimum covariance determinant), or `"mve"` (minimum volume ellipsoid).
`detrend`	logical; if `FALSE`, the plot shows values of `D^2` vs. `\chi^2`. if `TRUE`, the ordinate shows values of `D^2 - \chi^2`
`pch`	plot symbol for points. Can be a vector of length equal to the number of rows in `x`.
`col`	color for points. Can be a vector of length equal to the number of rows in `x`. The default is the first entry in the current color palette (see `palette` and `par`.
`cex`	character symbol size for points. Can be a vector of length equal to the number of rows in `x`.
`ref.col`	Color for the reference line
`ref.lwd`	Line width for the reference line
`conf`	confidence coverage for the approximate confidence envelope
`env.col`	line color for the boundary of the confidence envelope
`env.lwd`	line width for the confidence envelope
`env.lty`	line type for the confidence envelope
`env.fill`	logical; should the confidence envelope be filled?
`fill.alpha`	transparency value for `fill.color`
`fill.color`	color used to fill the confidence envelope
`labels`	vector of text strings to be used to identify points, defaults to `rownames(x)` or observation numbers if `rownames(x)` is `NULL`
`id.n`	number of points labeled. If `id.n=0`, the default, no point identification occurs.
`id.method`	point identification method. The default `id.method="y"` will identify the `id.n` points with the largest value of abs(y-mean(y)). See `showLabels` for other options.
`id.cex`	size of text for point labels
`id.col`	color for point labels
`xlab`	label for horizontal (theoretical quantiles) axis
`ylab`	label for vertical (empirical quantiles) axis
`main`	plot title
`what`	the name of the object plotted; used in the construction of `main` when that is not specified.
`ylim`	limits for vertical axis. If not specified, the range of the confidence envelope is used.

Details

cqplot is a more general version of similar functions in other packages that produce chi square QQ plots. It allows for classical Mahalanobis squared distances as well as robust estimates based on the MVE and MCD; it provides an approximate confidence (concentration) envelope around the line of unit slope, a detrended version, where the reference line is horizontal, the ability to identify or label unusual points, and other graphical features.

The method for "mlm" objects applies this to the residuals from the model.

The calculation of the confidence envelope follows that used in the SAS program, http://www.datavis.ca/sasmac/cqplot.html which comes from Chambers et al. (1983), Section 6.8.

The essential formula is

SE ( z_{(i)} ) = \hat{\delta} /g ( q_i)) \times \sqrt{ p_i (1-p_i) / n }

where z_{(i)} is the i-th order value of D^2, \hat{\delta} is an estimate of the slope of the reference line obtained from the corresponding quartiles and g(q_i) is the density of the chi square distribution at the quantile q_i.

Note that this confidence envelope applies only to the D^2 computed using the classical estimates of location and scatter. The car::qqPlot() function provides for simulated envelopes, but only for a univariate measure. Oldford (2016) provides a general theory and methods for QQ plots.

Value

Returns invisibly the vector of squared Mahalanobis distances corresponding to the rows of x or the residuals of the model for the identified points, else NULL

Author(s)

Michael Friendly

References

J. Chambers, W. S. Cleveland, B. Kleiner, P. A. Tukey (1983). Graphical methods for data analysis, Wadsworth.

R. W. Oldford (2016), "Self calibrating quantile-quantile plots", The American Statistician, 70, 74-90.

Examples



cqplot(iris[, 1:4])

iris.mod <- lm(as.matrix(iris[,1:4]) ~ Species, data=iris)
cqplot(iris.mod, id.n=3)

# compare with car::qqPlot
car::qqPlot(Mahalanobis(iris[, 1:4]), dist="chisq", df=4)


# Adopted data
Adopted.mod <- lm(cbind(Age2IQ, Age4IQ, Age8IQ, Age13IQ) ~ AMED + BMIQ, 
                  data=Adopted)
cqplot(Adopted.mod, id.n=3)
cqplot(Adopted.mod, id.n=3, method="mve")


# Sake data
Sake.mod <- lm(cbind(taste, smell) ~ ., data=Sake)
cqplot(Sake.mod)
cqplot(Sake.mod, method="mve", id.n=2)

# SocialCog data -- one extreme outlier
data(SocialCog)
SC.mlm <-  lm(cbind(MgeEmotions,ToM, ExtBias, PersBias) ~ Dx,
               data=SocialCog)
cqplot(SC.mlm, id.n=1)

# data frame example: stackloss data
data(stackloss)
cqplot(stackloss[, 1:3], id.n=4)                # very strange
cqplot(stackloss[, 1:3], id.n=4, detrend=TRUE)
cqplot(stackloss[, 1:3], id.n=4, method="mve")
cqplot(stackloss[, 1:3], id.n=4, method="mcd")

heplots documentation built on May 29, 2024, 9:24 a.m.